However, a packed chart built on an atomic CFG does not make available all of the features that are important to many CFG-based SRL systems.

the very useful treepath feature, which lists the categories touched by walking the tree from the predicate to the target word, only makes sense when you have a complete tree, so cannot easily be computed from the chart (  be lexicalized with their headwords, and this information would be useful in role labeling -but even this misses vital subcategorization information that would be available in the complete parse.


We do this by requiring that it has a score that is within a factor of α of the best available.

the single-best parse from the semantic model must satisfy log P (sem) > log P (baseline) + log(α) where the α value is tuned on the development set.


For this reason, we also employ a bag-of-words model.

each context c i is associated with a vector g i that contains the nouns kept as result of the corpus preprocessing stage.


In this manner D partitions the edges that exist in G. The primary assumption in the hierarchical random graph model is that edges in G exist independently, but with a probability that is not identically distributed.

the probability that an edge {v i , v j } exists in G is given by a parameter θ k associated with D k , the lowest common ancestor of v i and v j in D. In this manner, the topological structure D and the vector of probabilities θ define the HRG given by H(D, θ) (Clauset et al., 2008).


We observe that 4(A) is more likely than 4(B), since it provides a better division of the network leaves.

the likelihood of (1 − 0.5) 2 ) = 0.062.


Following the work in (Clauset et al., 2008), we pick a set of transitions between dendrograms, where a transition is a re-arrangement of the subtrees of a dendrogram.

given a current dendrogram D curr , each internal node D k of D curr is associated with three subtrees of D curr .


The similarity between two vertices is calculated according to the degree of connectedness among their direct neighbours.

we would like to assign high similarity to pairs of vertices, whose neighbours are close to forming a clique.


In both figures, we observe that HRGs outperform the CWU baseline under all parameter combinations.

all of the 12 performance differences for p 3 = 0.09 are statistically significant using McNemar's test at 95% confidence level, while for p 3 = 0.05 only 2 out of the 12 performance differences were not judged as significant from the test.


The picture is the same for p 3 = 0.13, where CWU performs significantly worse than for p 3 = 0.05 and p 3 = 0.09.

the largest performance difference between HRGs and CWU is 9.4% at p 1 = 25, p 2 = 10 and p 3 = 0.13.


In Figures 6(A) and 6(B) we observe that HRGs perform significantly better than HAC.

all of their performance differences are statistically significant for these parameter values.


However, this picture is not the same when considering a higher context similarity threshold (p 3 = 0.13) as Figure 7 shows.

hRGs and hAC perform similarly for p 3 = 0.13, while the majority of performance differences are not statistically significant.


The first approach models argument consistency based on implicit modeling of the relationship between a verb and its aligned subject/object.

given a pair of verb terms (x, y) where x is from the conversation segment and y is from the hypothesis, let s y be the subject of y and s x be the aligned entity of s y in the conversation (in case of multiple alignments, s x is the one closest to x).


Here we want to study how they interact with other entailment components and what is their effect in the enhanced modeling of long distance relations.

we test the performance of implicit and explicit modeling of long distance relations under two different representation settings: the basic representation and the augmented representation.


Aiming at preventing the OOV recall from dropping sharply and still maintaining an overall performance as good as that of the state-of-art segmenter when working with heterogeneous test sets, we propose in this paper to use a semisupervised way for Chinese word segmentation task.

we propose to use χ 2 statistics together with bootstrapping strategies to build Chinese word segmentation model.


To keep the IV recall from falling, we propose to use a bootstrapping method.

we choose to use both models with χ 2 features and without χ 2 features.


We call these vectors latent descriptors.

each object to be clustered, i.e., each word type w, is described in terms of its left-tag context and right-tag context.


A straightforward approach to this latentdescriptor K-means problem is to adapt the classical iterative K-means algorithm so as to handle the latent descriptors.

in each iteration, given the assignment A obtained from the previous iteration, one first computes the latent descriptors for all word types as defined above, and then proceeds in the usual way to update cluster centroids and to find a new assignment A to be used in the next iteration.


We experimented with this hard-assignment latentdescriptor clustering algorithm (data not shown), and found that a number of additional devices were necessary in order to make it work properly.

we found it necessary to use reduced-rank SVD on each iteration of the algorithm-as opposed to just the first iteration in the version presented here-and to gradually increase the rank r. Further, we found it necessary to include only the most frequent words at the beginning, and only gradually incorporate rare words in the algorithm.


Our per-task decoders are beam decoders, with beam-size b.

we limit the number of per-stage back-pointers to b due to the large size of the tagset for some of our sub-models.


The discriminative models are able to utilize prosodic features directly, enabling the use of contextual interactions with other features to further improve tagging accuracy.

in addition to the standard set of features used in the tagging literature, we use the feature templates presented in Table 3, where each feature associates the break b i , word w i , or some combination of the two with the current tag t i 4 .


Given that prosodic breaks were effective knowledge sources for parsing, an important goal of this work was to evaluate their impact on various tagging model configurations.

we have examined the use of prosodic information for tagging conversational speech with several different discriminative and generative models across three different speech transcript segmentations.


Instead, we condition on the type-level tag assignments T .

let S t = {i|T i = t} denote the indices of the word types which have been assigned tag t according to the tag assignments T .


Its only purpose is : Token word seqs (obs) w to explore how well we can induce POS tags using only the one-tag-per-word constraint.

the lexicon is generated as: This model is equivalent to the standard HMM except that it enforces the one-word-per-tag constraint.


(2010), we adopt a simpler naïve Bayes strategy, where all features are emitted independently.

we assume each word type W consists of feature-value pairs (f, v).


During training, we treat as observed the language word types W as well as the token-level corpus w. We utilize Gibbs sampling to approximate our collapsed model posterior: Note that given tag assignments T , there is only one setting of token-level tags t which has mass in the above posterior.

for the ith word type, the set of token-level tags associated with token occurrences of this word, denoted t (i) , must all take the value T i to have non-zero mass.


Models To assess the marginal utility of each component of the model (see Section 3), we incrementally increase its sophistication.

we evaluate three variants: The first model (1TW) only encodes the one tag per word constraint and is uniform over type-level tag assignments.


We report results for the best and median hyperparameter settings obtained in this way.

for both settings we report results on the median run for each setting.


We explore the task of automatically classifying dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service.

we investigate the effectiveness of various features and machine learners for this task.


Our motivation for using structural information as a feature is that the location of an utterance can be a strong predictor of the dialogue act.

dialogues are sequenced, comprising turns (i.e.


Finally, we tested the combination of features from structural and dependency information.

we used a base feature (unigrams with Boolean value), relative position, author information, combined with each of the different dependency features -LabelList, LabelPrev, LabelAuthor, LabelPrev t and LabelAuthor t .


Looking over the data, STATEMENT is a common source of misclassification, as it is the majority class in the data.

ly, a large number of RE-QUEST and RESPONSE ACK utterances were tagged as STATEMENT.


This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification.

all the three sub-tasks are addressed using tree kernel-based methods with appropriate syntactic parse tree structures.


As the only work we can find, Ferrandez and Peral (2000) proposed a hand-engineered rule-based method for both anaphoricity determination and antecedent identification.


(2006) employed a convolution tree kernel on anaphora resolution of pronouns.

a document-level syntactic parse tree for an entire text was constructed by attaching the parse trees of all its sentences to a newadded upper node.


For each sub-task, different parse tree structures are constructed.

the context-sensitive convolution tree kernel, as proposed in , is employed to compute the similarity between two parse trees via the SVM toolkit SVMLight.


In the tree kernel-based framework, we perform the three sub-tasks, zero anaphor detection, anaphoricity determination and antecedent identification in a pipeline manner.

given a zero anaphor candidate Z, the zero anaphor detector is first called to determine whether Z is a zero anaphor or not.


This phenomenon has also been employed in Zhao and Ng (2007) in generating zero anaphor candidates.

if the predicate phrase node occurs in a coordinate structure or is modified by an adverbial node, we only need to consider its parent.


The intuition behind our parser tree structure for zero anaphor detection is to keep the competitive information about the predicate phrase node and the zero anaphor candidate as much as possible.

the parse tree structure is constructed by first keeping the path from the root node to the predicate phrase node and then attaching all the immediate verbal phrase nodes and nominal phrase nodes.


Be-sides, since all the anaphoric zero anaphors have their antecedents at most one sentence away, we only consider antecedent candidates which are at most one sentence away.

a documentlevel parse tree for an entire document is constructed by attaching the parse trees of all its sentences to a new-added upper node, as done in Yang et al.


In comparison, we propose a tree kernel-based unified framework for all the three sub-tasks in zero anaphora resolution.

different parse tree structures are constructed for different sub-tasks.


However, existing corpora have their limitations.

the documents in these corpora tend to be limited in length and, in consequence, discourse structure.


Although lexical dissimilarity is seemingly the easiest to judge automatically among the three, it poses an interesting challenge to automatic evaluation metrics, as overlap with the reference has been the basis of almost all evaluation metrics.

while MT evaluation and paraphrase evaluation are conceptually closely related, the latter actually highlights the deficiencies of the former, namely that in most automatic evaluations, semantic equivalence is underrepresented and substituted by lexical and syntactic equivalence.


We observe here that the overall assessment b is not a linear combination of the three measures.

a high dissimilarity score is meaningless by itself.


Errors Caused by Search Space Pruning Recall from Section 1.1 that Moses uses several heuristics to prune the search space.

there is a distortion limit and a limit on the number of target phrases considered for one source phrase.


Various extensions of the baseline ILP program have been suggested and/or evaluated.

the ILP formalism lends itself well to expressing various constraints that are typically used in conventional PBTS.


First, we find one-to-one corresponding words.

we find words that appear in both sentences and only once in each sentence.


The training data are thus no longer well-formed or clean, but augmented with L1 error statistics.

each preposition p i in training is replaced with a different preposition p j with probability probConf, s.t.


The number of components n = |x| varies between instances.

each part of an instance is labelled from a finite set Y, |Y| = K. a labeling of an entire instance belongs to the product set y ∈ Y × Y .


A brute-force approach evaluates the value of the score µ • Φ(x, z) for each possible labeling z ∈ Y n , which is not feasible for large values of n. Instead, we follow standard factorization and restrict the joint mapping to be of the form, .

the mapping is a sum of mappings, each taking into consideration only a label of a single part, or two consecutive parts.


In this case, we draw K labelings from this distribution.

we exploit the Gaussian distribution over weight vectors w ∼ N (µ, Σ) maintained by AROW and CW, by inducing a distribution over labelings given an input.


For NER Dutch dataset CRF and CW have almost same number of erroneous words and almost same number of erroneous words detected, and finally in NER Spanish dataset CRF has fewer erroneous words and it detected less erroneous words.

where there are more erroneous words to find (e.g.


For each of the four datasets and the five algorithms we grouped the words according to the value of their confidence.

we used twenty (20) bins dividing uniformly the confidence range into intervals of size 0.05.


There are systematic differences between CoNLL'X and Stanford dependencies, e.g., as far as the representation of passive constructions, the position of auxiliary and modal verbs, or coordination representation is concerned.

the representation of the passive construction and the role of the auxiliary verb therein may have considerable effects for semantics-sensitive tasks.


These mentions have only been tagged correctly when we have added the new extracted features to our model.

the error-analysis clearly points out that one possible way to get further improvement is to increase the parallel data in order to increase the number of matches between (1) the number of mentions which are wrongly tagged by the TL MD model and (2) the number of mentions in the TL side of the parallel corpus.


Motivated by these results, we explore the following natural question in this paper: Are rule-based systems still a viable approach to named-entity recognition?

we have designed and implemented a high-level language NERL on top of Sys-temT, a general-purpose algebraic information extraction system.


In this paper, we explore the following natural question: Are rule-based systems still a viable approach to named-entity recognition?

(a) Is it possible to build, maintain and customize rule-based NER annotators that match the state-of-the-art results obtained using machine-learning techniques?


These languages are not constrained by the requirement that all rules map onto finite state transducers, and therefore can express a significantly richer semantics than grammar-based languages.

the AQL rule language as implemented in SystemT  can express many common operations used in rule-based information extraction without requiring custom code.


The design of NERL draws on our experience with building and customizing multiple complex NER annotators.

we have identified the operations required in practice for such tasks, and expose these operations as built-in constructs in NERL.


2 We note that the extraction quality of CoreNER orig was low on CoNLL03 and ACE05 mainly due to differences in entity type definitions.

sports organizations, which occurred frequently in the CoNLL03 collection, were not considered during the development of CoreNER orig , while in ACE05, ORG and LOC entity types were split into four entity types (Organization, Location, Geo-Political Entity and Facility).


We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text.

we tackle relation extraction and entity identification jointly.


On inspection, it turns out that many of the errors can be easily identified: they amount to violations of basic compatibility constraints between facts.

we observe unsatisfied selectional preferences of relations towards particular entity types as types of their arguments.


The hidden output variables of our model are Y = (Y c ) c∈C .

we have one variable Y c for each candidate tuple c ∈ C .


However, instead extracting facts in isolation, we model interactions between facts in order to improve precision.

we capture selectional preferences of relations.


On analysis, we found that most of these correspond to the ACE event type CONTACT.

the "meeting" event, which is an ACE CONTACT event and an INR event according to our definition, is the major cause of overlap.


This sampling technique proposes to generate synthetic examples of the minority class by "perturbing" the training data.

ha and Bunke (1997) produced new synthetic examples for the task of handwritten character recognition by doing operations like rotation and skew on characters.


Second, it does not require solving the sentence-level task perfectly, and also does not require precise sentence-level training labels.

our goal is to learn to identify the informative (subjective) sentences that best explain the training labels to the extent required for good document classification performance.


Then all the features initially available during training are generated from these objective sentences and are thus useless for sentiment classification.

too much useful information has been suppressed for the model to make effective decisions.


Notice that most of these "objective" sentences can plausibly belong to speeches made in opposition to bills that limit stem cell research funding.

they do not clearly indicate the speaker's stance towards the specific bill in question.


This is a very generic representation for model translation and the model m could be any type of text classification.

in this paper, we take the Maximum Entropy (ME) model (Berger et al., 1996) as an example for the model translation across languages, since the ME model is one of the most widely used text classification models.


To alleviate this problem, we employ semisupervised learning in order to adapt the model to the target language.

we first start by using the translated classifier from English as an initial classifier to label a set of Chinese documents.


In this work, we conduct a systematic comparative study of what we search, what we read, what we write, and what we tag in the scenarios of bursty events.

we analyze the language used in different contexts of bursty events, including two different query log contexts, two news media contexts, two blog contexts, and an additional context of social bookmarks.


Then we cluster question vectors using a simple single-pass clustering algorithm (Frakes and Yates, 1992).

for each question, we compute its similarity with each centroid of existing cluster.


In this paper, we follow the notion of ideology as defines by Van Dijk in (Dijk, 1998) as "a set of general abstract beliefs commonly shared by a group of people."

an ideology is a set of ideas that directs one's goals, expectations, and actions.


In addition, we utilize an ideologyspecific topic Ω v which is again a multinomial distribution over the same vocabulary.

ω v models the abstract belief shared by all the documents written from view v. if v denotes the liberal perspective, then ω v gives high probability to words like progressive, choice, etc.


More recently, Reisinger and Mooney (2010) present a method that uses clustering to produce multiple sense-specific vectors for each word.

a word's contexts are clustered to produce groups of similar context vectors.


In keeping with the formulation in Section 3 we opt for a probabilistic interpretation of NMF (Gaussier and Goutte, 2005;Ding et al., 2008) and thus minimize the KL divergence between W H and V .

we interpret matrix V as V ij = P (t i , c j ), and matrices W and H as P (t i , z k ) and P (c j |z k ), respectively.


Using the tiered clustering model, we derive a multi-prototype representation capable of capturing varying degrees of sharing between word senses, and demonstrate its effectiveness in lexical semantic tasks where such sharing is desirable.

we show that tiered clustering outperforms the multi-prototype approach for (1) selectional preference (Resnik, 1997;Pantel et al., 2007), i.e.


In this section, we calibrate the single prototype and multiprototype methods on WS-353, reaching the limit of human and oracle performance and demonstrating robust performance gains even with semantically impoverished features.

we obtain ρ0.75 correlation on WS-353 using only unigram collocations and ρ0.77 using a fixed-K multiprototype representation (Figure 3; Reisinger and Mooney, 2010).


Although a higher precision can be achieved by using a higher threshold value, for example noun's substitutions almost reach 90% precision with threshold equal to 0.9, the large drop in recall means many applicable synonyms are being eliminated.

the trade-off between precision and recall implies the trade-off between imperceptibility and payload capacity for linguistic steganography.


Phrase-structure parser induction is made difficult due to two types of ambiguity: the constituent structure and the constituent labels.

the constituent labels are highly ambiguous, firstly we don't know a priori how many there are, and secondly labels that appear high in a tree (e.g., an S category for a clause) rely on the correct inference of all the latent labels below them.


valency conditioning, interpolated back-off smoothing and a random initialiser.


Further gains in performance may come from an exploration of the backoff paths employed within the model.

more extensive experimentation with alternate priors and larger training data may allow the removal of the lexicalisation cutoff which is currently in place to counter sparsity.


In this work, since parameter estimation was not our focus, we set the hyperparameters to be approximately 1 K , where K is the number of possible values, according to the rule of thumb common in the literature.


For each unique coarse symbol s we tie together the distributions π ss z c for all possible parent and context combinations (i.e., s , z , and c) using a Hierarchical Dirichlet Process (HDP).

for a single s, each distribution π ss z c over subsymbols is drawn from a DP with concentration parameter α and base distribution β s over subsymbols.


We encode more detailed valence information than Klein and Manning (2004) and condition child generation on parent valence.

after drawing a node we first decide whether to proceed to generate a child or to stop conditioned on the parent symbol and subsymbol and the current context (direction and valence).


In the absence of subsymbol refinement (i.e., when subsymbol z is set to be identical to coarse symbol s), our model simplifies in some respects.

the HDP gener-ation of z is obviated and word x is drawn from a word distribution φ s indexed solely by coarse symbol s. The resulting simplified model closely resembles DMV (Klein and Manning, 2004), except that it 1) explicitly generate words x rather than only partof-speech tags s, 2) encodes richer context and valence information, and 3) imposes a Dirichlet prior on the symbol distribution θ.


PGI is more relevant as a point of comparison, since it is able to leverage multilingual data to learn information similar to what we have declaratively specified using universal rules.

pGI reduces induction ambiguity by connecting language-specific parameters via phylogenetic priors.


Previous work has also used WordNet, a lexical database of English, to identify word polarity.

hu and Liu (2004) use WordNet synonyms and antonyms to predict the polarity of any given word with unknown polarity.


A natural question to ask is what happens when we use someother language, say Hebrew or Russian or Tamil, instead of Hindi.

does the retrieval performance, on an average, vary substantially with the conjugate language?


The bestOut (c) routine exploits the fact that the constraints of Model 1 only act on the label for trigger i and its outgoing edges.

enforcing consistency between e i,t and outgoing edges a i,j,r has no effect on consistency between e l,t and a i ,j ,r for any other trigger i = i.


For f B (p, q) we re-use the token pair representations from f R .

we create one active feature for each element in feats (p, q).


The main idea will be to replace bit-strings (as described in the previous section) by a much smaller number of dynamic programming states.

the states of the new dynamic program will be tuples (w 1 , w 2 , n, l, m, r).


All of our parsing models are based on the transition-based dependency parsing paradigm (Nivre, 2008).

all models use an arc-eager transition strategy and are trained using the averaged perceptron algorithm as in Zhang and Clark (2008) with a beam size of 8.


In order to take advantage of treebanks in multiple languages, our multi-source system simply concatenates the training data from all non-target languages.

the multi-source direct transfer parser for Danish will be trained by first concatenating the training corpora of the remaining eight languages, training a delexicalized parser on this data and then directly using this parser to analyze the Danish test data.


But again, there is an overall trend to better models.

starting with an English-only direct transfer parser with 57.0% UAS on average, by adding parallel corpora and multiple source languages we finish with parser having 63.8% UAS on average, which is a relative reduction in error of roughly 16% and more than doubles the performance of a DMV model (Table 1).


One fundamental point the above experiments illustrate is that even for languages for which no resources exist, simple methods for transferring parsers work remarkably well.

if 6 Note that the last set of results was obtained by using the same English training data as Ganchev et al.


In this paper, we propose a bitext parsing approach in which we produce the bilingual constraints on existing monolingual treebanks with the help of SMT systems.

we aim to improve source-language parsing with the help of automatic translations.


This nominalization is a relative clause, but does not have a clue about its boundary.

it is very hard to determine which word is the head of " (jiqiao)/skill".


We used articles 301-325 for development, 271-300 for testing, and the other articles for training.

we evaluated the systems on the same test data as CTB2 tp .


When enforcing equation 2, that gives the Relative Frequency Estimate (RFE): Unlike RFE for PCFGs, however, the RFE for PTSGs has no clear probabilistic interpretation.

it does not yield the maximum likelihood solution, and when used as an estimator for an allfragments grammar, it is strongly biased since it assigns the great majority of the probability mass to big fragments (Johnson, 2002).


Notice that although we used only the 20 applicable paraphrasing rules with the highest scores to generate the S, C pairs, less than half of the candidate paraphrases (C) were considered good, and approximately only 20% perfect.

applying paraphrasing rules (even only those with the 20 best scores) to each input sentence S and randomly picking one of the resulting candidate paraphrases C, without any further filtering (or ranking) of the candidates, would on average produce unacceptable paraphrases more frequently than acceptable ones.


Work in contextsensitive spelling error correction (Golding and Roth, 1999) has traditionally focused on confusion sets with similar spelling (e.g., {dessert, desert}) or similar pronunciation (e.g., {there, their}).

the words in a confusion set are deemed confusable because of orthographic or phonetic similarity.


In contrast, we investigate in this paper a class of grammatical errors where the source of confusion is the similar semantics of the words, rather than orthography, phonetics, or syntax.

we focus on collocation errors in EFL writing.


Then we compute the probability that v belongs to C in WordNet given that (u, v) is an edge in the instance network and u belongs to C in WordNet: We compare this to the background probability P r b = Pr[v ∈ T C | u, v ∈ V and u ∈ T C ], which gives the probability that v belongs to C in WordNet if it is chosen at random.

if P r h = 1, this means that whenever u has the label C and (u, v) is an edge, then v is always labeled with C. If indeed this is the case, then a good classifier can simply take the initial set L and extend the labels to all nodes reachable from L in the semantic network.


The expected accuracy for class C is given by n , where n is the number of nodes in the network, and T C is the set containing all nodes that belong to C according to WordNet.

given that there are 84 nodes in the network that are classified as invertebrate according to WordNet, and there are 397 nodes in total, if we choose any number of nodes at random our expected accuracy is 21%.


While the obtained aspects may contain some synonym terms, such as "earphone" and "headphone", we further perform synonym clustering to get unique aspects.

we first expand each aspect term with its synonym terms obtained from the synonym terms Web site 2 , then cluster them to obtain unique aspects based on unigram feature.


These features are generated based on auxiliary documents collected from Web.

we issue each aspect term and aspect term pair as queries to Google and Wikipedia, respectively, and collect the top 100 returned documents of each query.


Moreover, we perform sentiment classification to determine consumer opinions on various aspects.

we train a SVM sentiment classifier based on the Pros and Cons reviews described in Section 2.3.


We conducted a leave-one-out study to evaluate the effectiveness of each optimization criterion.

we set one of the tradeoff parameters (λ 1 , λ 2 , λ 3 ) in Eq.


For example, the adverb "very" in "very brilliant" of D 1 was not important for discourse recognition.

the number of mismatches in match(d i , d j ) could not precisely reflect the confidence of the generated common SSRs.


Moreover, these words did not belong to the 4 kinds of most representative words.

the weights of most mismatches were calculated using the equation presented in Section 4.2 instead of utilizing a unified value, i.e.


In contrast, our proposed method for phraselevel sentiment analysis is inspired by recent work on distributional approaches to compositionality.

baroni and Zamparelli (2010) tackle adjective-noun compositions using a vector representation for nouns and learning a matrix representation for each adjective.


Instead of taking the 1best parse, we produce a ranked n-best list of predictions and select the parser which gives the best score according to an external evaluation function.

instead of relying on the intrinsic model score, we use an extrinsic score to select the parse towards Algorithm 1 Select parse that maximizes an extrinsic metric.


In this paper we focus our attention on machine translation as the final application, but one could envision applying our techniques to other applications such as information extraction or question answering.

we explore one application of targeted self-training, where computing the extrinsic metric involves plugging the parse into an MT system's reordering component and computing the accuracy of the reordering compared to a reference word order.


We converted the treebank to match the tokenization expected by our MT system.

we split tokens containing hyphens into multiple tokens and, somewhat simplistically, gave the original token's part-of-speech tag to all newly created tokens.


The first model induces a binary parse, which defines the space of possible reorderings.

only trees that properly separate verbs from their object noun phrases will license an SVO to SOV transformation.


The second term in the gradient-the expected feature vector-can be computed efficiently because the feature vector φ(t ) decomposes over the spans of t .

the inside-outside algorithm provides the quantities needed to compute the posterior probability of each terminal span [k, ) and each split non-terminal (k, m, ).


The differences stem primarily from the different purpose of the model: here, features are not meant to decide how to reorder the sentence, but instead how to bracket the sentence hierarchically so that it can be reordered.

terminal spans have features on the sequence of POS tags and word clusters they contain, while a split non-terminal (k, m, ) is scored based on the tags/clusters of the following words and word pairs: e k , e m−1 , e m , e −1 , (e k , e m ), (e k , e −1 ), and (e m−1 , e m ).


Despite these differences, certain integrated reordering models are similar in character to syntactic pre-ordering models.

the tree rotation model of Yamada and Knight (2001) posited that reordering decisions involve rotations of a source-side syntax tree.


*or**=significantly better than s2t system (p<0.05 or 0.01 respectively).

very similar the FT2ET system with deep similarity matching obtains the best translation quality in all tasks and surpasses the baseline s2t system by 0.93 BLEU points in development data and by more than 1.0 BLEU point in test sets.


We will embed this function in a posterior predictive check and use it to evaluate and visualize topic models in new ways.

we develop discrepancy functions for latent Dirichlet allocation (the simplest topic model) that measure how well its statistical assumptions about the topics are matched in the observed corpus and inferred topics.


In complicated Bayesian models, such as topic models, Bayesian model checking can point to the parts of the posterior that better fit the observed data set and are more likely to suggest something meaningful about it.

we will develop posterior predictive checks (PPC) for topic models.


The top left topic represents formulaic language, language that occurs verbatim in many documents.

it models the boilerplate text "Here is a selective listing by critics of The Times of new or noteworthy..." Identifying repeated phrases is a common phenomenon in topic models.


However, with a small amount of linguistic side information (Druck et al., 2009;Naseem et al., 2010), we see that these issues can be overcome.

we see that large data + a little linguistics > fancy unsupervised learning algorithms.


In our second approach, BALANCED, we normalize the three components to "compete" equally.

we scale and translate all three components to have zero mean and unit variance, and set the αs to all be equal to one.


Finally, our third approach, SEMISUP, is based on using a small amount of labeled data to set the parameters.

we use 10 labeled sentences to select parameters based on the same grid search as the OPTIMAL setting.


For a topic model with a generalized Pólya urn for the topic-word component, the sampling distribution is more complicated.

the topicword component of the sampling distribution is no longer a simple predictive distribution-when sampling a new value for z (d) n , the implication of each possible value for subsequent tokens and their topic assignments must be considered.


Although not thoroughly explored in this paper, our finding is related to an ongoing debate about the structure of the human sentence processor.

the model of Dubey (2010), which also simulates the effect of discourse on syntax, is aimed at examining interactivity in the human sentence processor.


Indeed, with a little algebra it can be shown that solving a maximum weighted matching problem with weights c ij that count potential substitution costs gives the correct update for x.

c ij is the total cost of substitution edits in the active edit alignment prob-lems that would result if source character i were not mapped to target character j in the alphabet matching x.


Here the lexicons in source and target languages are extracted from corpora by taking the top 2,000 words in each corpus.

we used the English and Spanish sides of the Europarl parallel cor-pus (Koehn, 2005).


*Note that results for this system are on a somewhat different task.

the MATCHER system assumes the inventories of cognates in both Hebrew and Ugaritic are known, while the system of Snyder et al.


Note that while the evaluation data sets for our two models are the same, the tasks are very different.

our system assumes the inventories of cognates in both Hebrew and Ugaritic are known, while the system of Snyder et al.


In this paper, we argue that languages for which we have gold-standard morphological analyses can be used as effective guides for languages lacking such resources.

instead of treating each language's morphological analysis as a de novo induction problem to be solved with a purely handcoded bias, we instead learn from our labeled languages what linguistically plausible morphological analyses looks like, and guide our analysis in this direction.


This is a kind of simple baseline to our later experiments, attempting to achieve the same effect by simpler means.

we use the algorithm of Huang (2008) to generate oracle F-measure parses for each sentence.


To address this problem, Hall and Klein (2010) propose an iterative bipartite matching scheme where one language is held out from the others, and then words are assigned to the remaining groups to maximize the probability of the attachment.

for some language and fixed assignments π − for the other languages, they seek an assignment π that maximizes: Unfortunately, while this approach was effective with only a few languages (they tested on three), this algorithm cannot scale to the eighteen languages in Formosan, let alone the hundreds of languages in Oceanic.


Second, we use an agglomerative inference procedure, which greedily merges cognate groups that result in the greatest gain in likelihood.

for all pairs of cognate groups g a with words w a and g b with words w b , we compute the score: This score is the difference between the log probability of generating two cognate groups jointly and generating them separately.


Innovations, on the other hand, are generated from a language model independent of the parent's word.

our generative process takes the following form: • For each gloss m, choose a root word W root ∼ λ, a language model over words.


3 Potentially, our parsimony model sacrifices a certain amount of power to make inference tractable.

it cannot model homoplasy, the presence of more than one word in a language for a given gloss.


Therefore, we apply the common trick of grouping variables to form a tree.

we group each word variable W with its innovation parameter I .


Therefore, we only track scores for words that actually exist in a given gloss, which means that internal nodes only have mass on those words.

if a gloss has 10 distinct words across all the languages in our dataset, we pass messages that only contain information about those 10 words.


We model the evolution of a word w to its child w as a sequence of unigram edits that include insertions, deletions, and substitutions.

we use a standard three-state pair hidden Markov model that is closely related to the classic alignment algorithm of Needleman and Wunsch (1970) (Durbin et al., 2006).


These parameters can be learned separately, though due to data sparsity, we found it better to use a tied parameterization as with the sound laws.

we fit a log linear model whose features are indicators on the specific language, as well as a global innovation parameter that is shared across all languages.


First, we report cluster purity, which is a kind of precision measure for clusterings.

each cluster is assigned to the cognate group that is the most common cognate word in that group, and then purity is computed as the fraction of words that  Combined System PARSIM Agg.


A translation model trained on any of these subcorpora can be comparable -or substantially better -than a translation system trained on the entire corpus.

the new bilingual Moore-Lewis method, which is specifically tailored to the machine translation scenario, is shown to be more efficient and stable for MT domain adaptation.


We establish this by experimenting with the Hansard   The last hypothesis we test is whether a better fitting language model yields a better machine translation system.

we expect the T-based LMs to outperform the Obased LMs when used as part of an MT system.


Table 12 shows that the original English LMs should be enlarged by a factor of ten to achieve translation quality similar to that of translationbased LMs.

much smaller translated LMs perform better than much larger original ones, and this is true for various LM sizes.


We do not seek to explain all their variants and intricate details, but rather to outline their core characteristics and to highlight their similarities and differences.

since all our experiments are based on single references, we omit the complications due to multiple references and refer our readers instead to the respective original papers for the details.


• In case |span(v)| > 1 add a new node u as a daughter designating the lexical head, labeled with the wildcard symbol *: , add a new node u as a daughter, labeled with its own terminal: to say, we label all nodes with spans greater than 1 with the grammatical function of their head, and for each node we add a new daughter u designating the head word, labeled with its grammatical function.


We compute the intersection of the edit script turning the parse tree into the generalize gold with the edit script turning the native gold tree into the generalized gold, and discard its cost.

if parse1 and parse2 are compared against gold1 and gold2 respectively, and if we set gold3 to be the result of gold1 t gold2, then δ new is defined as:  Now, if gold1 and gold3 are identical, then ES * (gold1,gold3)=∅ and we fall back on the simple tree edit distance score δ new (parse1,gold1,gold3)=δ(parse1, gold3).


For our evaluation, we used the Nov 4, 2010 release of XLE and the Nov 25, 2009 release of the ParGram English grammar, with c-structure pruning turned off and resource limitations set to the maximum possible to allow for exhaustive search.

we are evaluating the f-structures output by the system.


Rather than postulating a strictly synchronous structure over the source and target sentences, QG identifies a "sloppy" alignment of parse trees assuming that the target tree is in some way "inspired by" the source tree.

our model is formulated as an integer linear program and uses QG to capture the space of all possible rewrites.


We cast the problem of finding a suitable target simplification given a source sentence as an integer linear program (ILP).

simplified text is created from source sentence parse trees by identifying and applying QG grammar rules.


Summarization is an ideal solution to provide an abbreviated, informative reorganization for faster and better representation of news documents.

a timeline (see Table 1) can summarize evolutionary news as a series of individual but correlated component summaries (items in Table 1) and offer an option to understand the big picture of evolution.


We classify sentence relationships as interdate and intra-date dependencies.

the inter-date dependency calculation includes temporal decays to project sentences from all dates onto the same time horizon (Figure 1 (a)).


Furthermore, ETS has 8 free parameters to tune while ETTS has only 2 parameters.


4), a descriptive sentence for an image must contain at minimum: 1) the important objects (Nouns) that participate in the image, 2) Some description of the actions (Verbs) associated with these objects, 3) the scene where this image was taken and 4) the preposition that relates the objects to the scene.

a quadruplet of T = {n, v, s, p} (Noun-Verb-Scene-Preposition) that represents the core sentence structure.


Our focus instead is to show that with the addition of language to ground the noisy initial visual detections, we are able to improve the quality of the generated sentence as a faithful description of the image.

we show that it is possible to avoid predicting actions directly from images -which is still unreliable -and to use the corpus instead to guide our predictions.


Since it is possible that different verbs may be associated with varying number of object arguments, we limit ourselves to verbs that take on at most two objects (or more specifically two noun phrase arguments) as a simplifying assumption: N k = {n 1 , n 2 } where n 2 can be NULL.

n 1 and n 2 are the subject and direct objects associated with v ∈ V. Using this assumption, we can construct the set of verbs, V. To do this, we use human labeled descriptions of the training images from the UIUC Pascal-VOC dataset (sec.


A main shortcoming of using ROUGE-1 is that the generated sentences are compared only to a finite set of human labeled ground truth which obviously does not capture all possible sentences that one can generate.

rOUGE-1 does not take into account the fact that sentence generation is innately a creative process, and a better recall metric will be to ask humans to judge these sentences.


This property states that the reliability of any measure combination is lower bounded by the heterogeneity of the measure set.

a single measure can be more or less reliable, but a system improvement according to all measures in an heterogeneous set is reliable.


For instance, if H(X) = 0.9, then only for 10% of output pairs in the corpus there exists an improvement according to all measures.

unanimous evaluation results from heterogeneous measures are reliable but harder to achieve for the system developer.


In the paper (Conroy and Dang, 2008) the authors noted that while there is a significant gap in performance between machine systems and human summarizers when measured by average manual metrics, this gap is not present when measured by the averages of the best automatic metric (ROUGE).

in the DUC 2005-2007 data some systems have ROUGE performance within the 95% confidence intervals of several human summarizers, but their pyramid, linguistic, and responsiveness scores do not achieve this level of performance.


We begin with features for each of the quasi-synchronous configurations from Smith and Eisner (2006), adapted to phrase dependency grammars.

for a parent-child pair τ φ (i), i in τ φ , we consider the relationship between a(τ φ (i)) and a(i), the source-side phrases to which τ φ (i) and i align.


Although there is a great deal of recent research on extracting knowledge from text (Agichtein and Gravano, 2000;Etzioni et al., 2005;Snow et al., 2006;Pantel and Pennacchiotti, 2006;Yates et al., 2007), much less progress has been made on the problem of drawing reliable inferences from this imperfectly extracted knowledge.

traditional logical inference methods are too brittle to be used to make complex inferences from automatically-extracted knowledge, and probabilistic inference methods (Richardson and Domingos, 2006) suffer from scalability problems.


The parameter vector θ is estimated by maximizing a regularized form of the conditional likelihood of y given x.

we maximize the objective function where λ 1 controls L 1 -regularization to help structure selection, and λ 2 controls L 2 -regularization to prevent overfitting.


Through this combination, we attempt to improve on earlier work in Almuhareb (2006) and Hartung and Frank (2010), which are both embedded in a purely distributional setting.

we use Latent Dirichlet Allocation (LDA; Blei et al.


Being designed for exploratory rather than discriminative analysis, LDA does not intend conditioning of words or topics on external categories.

the resulting topics cannot be related to previously defined target categories.


Moreover, the model is constrained such that documents may be assigned only those topics that correspond to their observable category label(s).

latent topics t in the standard formulation of LDA (2) are constrained to correspond to explicit labels a.


C-LDA, at the other end of the spectrum, licenses semantic variability on both the attribute and the word level.

a word might be associated with some of the topics underlying an attribute, but not with all of them, and an attribute can be characterized by multiple topics.


Therefore, to reduce computation complexity during Gibbs sampling, we approximate the values of {z ij , s ij | i = j} by the topic/sense (z jj , s jj ) that are generated from window v j .

in Gibbs sampling, the algorithm does not actually sample the values of {z ij , s ij , | i = j}; instead, it directly assumes the sampled values are z jj , s jj .


To focus on instances of interest to us, we restricted ourselves to a subset of the rating data associated with relatively heavy raters.

we formed the experimental dataset by randomly selecting 9,003 raters who provided at least 200 ratings (of which at least 10 were positive and 10 were negative), 189,291 authors who received at least 20 ratings, and 5,088 news articles that received at least 40 comments in the raw dataset during the three-month period.


Our full model significantly outperforms strong baseline methods, as well as previous methods proposed for text recommendation.

learning weights over textual features across all users outperforms learning for each user individually, which holds true even for heavy raters.


We investigate the problem of response generation: given a conversational stimulus, generate an appropriate response.

we employ a large corpus of status-response pairs found on Twitter to create a system that responds to Twitter status posts.


We consider the problem of predicting measurable responses to scientific articles based primarily on their text content.

we consider papers in two fields (economics and computational linguistics) and make predictions about downloads and within-community citations.


3 To correct the initial segmentation given by the analyzer, we use non-parametric Bayesian language models that have been applied to unsupervised word segmentation .


(2009), we model the zerogram distribution P 0 with the word length k and the character sequence w = c 1 , • • • , c k .

we define P 0 as the combination of a Poisson distribution with mean λ and a bigram distribution over characters.


We evaluated the segmentation accuracy of 500 entry titles.

we evaluated the performance of a model with precision, recall and the F-score, all of which were based on tokens.


In Polish, for example, each lexical verb has literally 100 inflected forms (Janecki, 2000).

a single lexeme may be realized in a corpus as many different word types, which are differently inflected for person, number, gender, tense, mood, etc.


4 We evaluate whole paradigms as linguistic objects, following word-and-paradigm or realizational morphology (Matthews, 1972;Stump, 2001).

we presume that some language-specific distribution p(π) defines whether a paradigm π is a grammatical-and a priori likely-way for a lexeme to express itself in the language.


So, if the output distributions on lower stages change, the marginal probabilities of any fragments also change, and this in turn can change the value of the features on the upper stage.

the features on an upper stage indirectly depend on the models on the lower stages.


2It is worth noting that this formalization subsumes both directed and undirected linear-chain graphical models, which are the most typical models for sequence labeling, including HMM and CRF.

if the elements of V 1 are aligned into regular time slices, and the nodes in each time slice are associated with possible assignments of labels for that time, we obtain the representation equivalent to the ordinary linear-chain graphical models, in which all possible label assignments for each state are expanded.


Additional types of features can easily be incorporated into the model and inference procedure using the same general framework as in the basic model that uses only context word features.

we present two extensions to the basic model.


However, deficient models have proven useful in other unsupervised NLP tasks (Klein and Manning, 2002;Toutanova and Johnson, 2007).

toutanova and Johnson (2007) demonstrate good performance on unsupervised part-ofspeech tagging (using a dictionary) with a Bayesian model similar to our own.


We also explore the idea of extending the morphology feature space beyond suffixes, by including features like capitalisation and punctuation.

we use the features described in Haghighi and Klein (2006), namely initial-capital, containshyphen, contains-digit and we add an extra feature contains-punctuation.


For example, Riloff and Jones (1999) used a multi-level bootstrapping algorithm to learn both a semantic lexicon and extraction patterns, e.g., owned by X extracts COMPANY and facilities in X extracts LOCATION.

they learned semantic lexicons using extraction patterns, and then, alternatively, they extracted new patterns using these lexicons.


To address these issues, in this paper we devise a cost-effective methodology to create cross-lingual textual entailment corpora.

we focus on the following problems: (1) Is it possible to collect T-H pairs minimizing the intervention of expert annotators?


First, the generated corpora are perfectly suitable to produce entailment datasets similar to those used in the traditional RTE evaluation framework.

considering any possible entailment relation between two text fragments, our annotation subsumes the one proposed in RTE campaigns.


In our second experiment, we duplicate the setup of Birke and Sarkar (2006) so that we can compare our results with theirs.

a separate model is learned for each individual verb.


In the third experiment, we train the algorithm on the twenty-five new verbs that were not used by Birke and Sarkar (2006) and then we test it on the old verbs.

the algorithm is tested with verbs that it has never seen before.


Suppose r is the term corresponding to the i-th row in F and c is the term corresponding to the j-th column in F. Let c be marked left.

let f ij be the cell in the i-th row and j-th column of F. The numerical value in the cell f ij is the number of phrases found by Wumpus in which the center term was r and c was the unigram closest to r on the left side of r. f ij is the frequency with which r was found in the context c in our corpus.


In Table 3, we evaluate forests of fourgram trees produced using randomizations without degrading the tree construction algorithm.

we use random initializations of the Exchange algorithm and, additionally, variations in the training data fold.


This generates all possible tree fragments as features with the conditions that sibling nodes from the original trees cannot be separated.

substructures are composed by atomic building blocks corresponding to nodes along with all their direct children.


This generates all possible tree fragments, as above, but sibling nodes can be separated (so they can be part of different tree fragments).

a fragment is any possible tree path, from whose nodes other tree paths can depart.


In contrast, partial patterns, such as those provided by PTK applied to constituency (or dependency) trees, prove to be effective.

sTK has been considered as the best kernel for exploiting syntactic information in constituency trees, e.g.


In this paper, we show the effectiveness of statistical parsers for MWE identification.

tree Substitution Grammars (tSG) can achieve a 36.4% F1 absolute improvement over a state-of-theart surface statistics method.


In the Lexicon-Grammar, MWEs are classified according to their global POS tags (noun, verb, adverb, adjective), and described in terms of the sequence of the POS tags of the words that constitute the MWE (e.g., "N de N" garde d'enfant [guard of child] 'daycare', pied de guerre [foot of war] 'at the ready').

mWEs are represented by a flat structure.


Using a large text corpus for training, we collect statistics about what kinds of entities tend to occur as subjects of "play", and then rank the candidate entities according to their compatibility with the verb.

we employ the framework of (Thater10), which allows us to derive vector representations of words in syntactic contexts (such as being the subject of a particular verb).


Cosine similarity should be small if a phrase is non-compositional and large if it is compositional.

if the contexts of the candidate phrase are too dissimilar to the contexts of the sum of its parts or to the alternative phrases, then we suspect non-compositionality.


Comparison with the official results from KBP participants also shows that our system is competitive.

when no disambiguation text from the knowledge base is used, our system can achieve an overall 85.2% accuracy and 9.3% relative improvement over the best performance reported in KBP 2010.


Similar to the way we incorporate words from D Q into the query language model, we can also construct a feedback language model using the most likely official name of the query entity obtained from Wikipedia.

we define We can then linearly interpolate θ N g Q with the original query language model θ Q to form an expanded query language model θ G Q : Here G indicates that the query expansion comes from global world knowledge.


Here, we may benefit by leveraging more global knowledge, i.e, we should expand the N g Q with Wikipedia global context entities together with the title to fully benefit from global knowledge.

when KB text is not used, our system outperforms the highest TAC results for all three types of queries.


Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline.

the proposed prior has shown to be effective when coupled with discriminative features of the candidates.


However, most of them have neglected the following important observation: a single document or text segment tends to cover different slots rather than redundantly fill the same slot.

during clustering, candidates within the same text segment should be more likely to be distributed into different clusters.


Their algorithm assigned a candidate from a document to a cluster based on the candidate's feature similarity with candidates from other documents only.

the algorithm did not consider a candidate's relationships with other candidates in the same document.


We need a prior that gives a higher probability to a more diverse label assignment.

for a given y i for the i-th document, let n i,k be the number of candidates in the document that have been assigned to slot k. , where 1(•) is the indicator variable.


We extract the following features for every candidate: the candidate phrase itself, its head word, the unigram and bigram before and after the candidate in the sentence where it appeared, its entity type (person, location, organization, and date/time), as well as features derived from dependency parse trees.

we first apply the Stanford lexical parser to our data (de Marneffe et al., 2006).


While NB and LNLR are inferior to K-means for all three data sets, BMM shows mixed results.

bMM outperforms Kmeans for aviation incidents, but performs poorly for seminar announcements.


We evaluate the effectiveness of the proposed prior by combining it with GMM.


We propose a generative model that incorporates distributional prior knowledge about template slots in a document for the unsupervised IE task.

we propose a Poisson-based prior that prefers label assignments to cover more distinct slots in the same document.


These graphs suggest that the contribution of the individual feature types (semantic class information, partial patterns or infix words) to the classification performance is relatively minor, but in combination they do give a marked improvement over the base features, at least for some relations like causation and material.

the main contribution of semantic word classes and partial patterns to our method's performance lies not in the final classification step but seems to occur at earlier stages of the process, in the candidate and training data generation steps.


We, too, acknowledge the importance of combining different types of knowledge in the hard task of MWE identification.

we also believe that collocation measures are highly important for this task, but cannot completely solve the problem: linguistically-motivated features are mandatory in order to improve the accuracy of the classifier.


This makes IC highly dependant on the number of lexical variants.

iC needs several lexical variants in the pivot language per each entry sense in both dictionaries.


The underlying idea is to identify as translation equivalents those words which show similar distributions or contexts across two corpora of different languages, assuming that this similarity is proportional to the semantic distance.

establishing an equivalence between cross lingual semantic distance and translation probability.


In this respect, the cohesion penalty can be seen as an aid to the glue rule penalty and as an alternative to constituency-based constraints.

the glue rule penalty (Chiang, 2007) promotes hierarchical rules.


The baseline translation is fragmented and requires more glue rule applications.

it fails to translate the boxed area as a whole into "the relations between the palestinian national authority (pna) and the european union (eu)".


Our local language modeling approach achieves the highest BLEU scores for all test sets and across all genres.

the improvements of +1.06 BLEU for newswire documents are substantial.


More formally, we call two hyperedges e 1 and e 2 are competing hyperedges, if their corresponding rules r(e 1 ) = ⟨γ 1 , α 1 ⟩ and r(e 2 ) = ⟨γ 2 , α 2 ⟩ : This means they give different translations for a same source side.

we use C(e) to represent the set of competing hyperedges of e. Two derivations t 1 = ⟨V 1 , E 1 ⟩ and t 2 = ⟨V 2 , E 2 ⟩ are competing derivations if there exists e 1 ∈ E 1 and e 2 ∈ E 2 : 2 derivations t 1 and t 2 only differ in e 1 and e 2 , and these two hyperedges are competing hyperedges.


Furthermore, the resulting model assigns a score to the alignment and thus could be used in numerous rescoring algorithms, such as top-N rescorers.

we use the maximum entropy frame-work to choose alignment moves.


source documents similar to the test document and their corresponding target documents) in the training parallel corpus; 3) a topic cache, which stores the target-side topic words related with the test document in the source-side.

three new features are designed to explore various kinds of document-level information in above three kinds of caches.


While such a approach applies to both phrase-based and syntax-based SMT, this paper focuses on phrase-based SMT.

the static cache is employed to store relevant bilingual phrase pairs extracted from similar bilingual document pairs (i.e.


Besides, the possibility of choosing noisy/unnecessary bilingual phrase pairs in both the static and dynamic caches is wakened with the help of the topic words in the topic cache.

only the most similar document pair is used to construct the static cache and the topic cache unless specified.


Our dynamic cache is mostly inspired by Tiedemann (2010), which adopts a dynamic cache to store relevant bilingual phrase pairs from the best translation hypotheses of previous sentences in the test document.

a specific feature is incorporated S to capture useful documentlevel information in the dynamic cache:  4where i e - ¶ is a decay factor to avoid the dependence of the feature's contribution on the cache size.


To overcome above problems, we only employ the bilingual phrase pairs in the dynamic cache to inform the decoder whether one bilingual phrase pair exists in the dynamic cache or not, which is slightly similar to (Nepveu et al, 2004) ,thus avoiding extra computational burden and the fine-tuning of the decay factor.

following new feature is incorporated to better explore the dynamic cache: ∨ e =e ∧f =f ∧∥ e ∥>3 ∨ e =e ∧f =f ∧∥ e ∥>3 0 other Here, F is called the dynamic cache feature.


To overcome this problem, a static cache is included to store relevant bilingual phrase pairs extracted from similar bilingual document pairs in the training parallel corpus.

a static cache feature F is designed to capture useful information in the static cache in the same way as the dynamic cache feature, shown in Formula (5).


For this purpose, all the document pairs in the training parallel corpus are aligned at the phrase level using 2-fold cross-validation.

we adopt 50% of the training parallel corpus to train a model using Moses and apply the model to enforce phrase alignment of the remaining training data, and vice versa.


Among all variables, w ij is the only observable variable with all the other variables latent.

k denotes the number of topics considered in the model and φ is a k*V (V is the dimension of the vocabulary) Markov matrix each line of which denotes the word distribution of a topic.


Finally, we load some topic words corresponding to this topic z into the topic cache.

our LDA model deploy the setting of K=15, α=0.5 and β=0.1.


Table 3 shows the statistics of these data sets (with document boundaries annotated).

the sizes of the static, topic and dynamic caches are fine-tuned to 2000, 1000 and 5000 items, respectively.


As a result, we will modify our objective function of (4) to take yet another expectation over the unknown y.

we will replace L(δ Now, the minimum imputed empirical risk objective of (4) becomes If the loss function L(y, y i ) is a negated BLEU, this is equivalent to performing minimum-risk training described by (Smith and Eisner, 2006;Li and Eisner, 2009) on the imputed data.


The simplest way to do this is to require complete isomorphism of the SCFG trees used for the reverse and forward translations.

this does round-trip imputation (i.e., from y to x, and then to y ) at the rule level.


One can estimate θ by maximizing the loglikelihood of the data If the x i 's are missing, EM tries to iteratively maximize the marginal probability: The E-step of each iteration comprises computing , the expected loglikelihood of the complete data, where p θt (x | y i ) is the conditional part of p θt (x, y i ) under the current iterate θ t , and the M-step comprises maximizing it: Notice that if we replace p θt (x|y i ) with p φ (x | y i ) in the equation above, and admit negated loglikelihood as a loss function, then the EM update (14) becomes identical to (4).

the minimum imputed risk approach of Section 3.1 differs from EM in (i) using an externally-provided and static p φ , instead of refining it at each iteration based on the current p θt , and (ii) using a specific loss function, namely negated log-likelihood.


We include ten features that are standard in Hiero (Chiang, 2007).

these include one baseline language model feature, three baseline translation models, one word penalty feature, three features to count how many rules with an arity of zero/one/two are used in a derivation, and two features to count how many times the unary and binary glue rules in Hiero are used in a derivation.


Since the nearest neighbour property is asymmetric, we take the union of all the selected word pairs.

we retain a word pair, if either the Spanish word is in the top ranked list of the English word or vice versa.


In this section we propose to combine them.

we propose to augment the set of selected bilingual word pairs using the monolingual word pairs.


We currently study natural language processing applications.

we are in the process of devising homotopy methods for domain adaptation Blitzer (2008) and language modeling based on context tree weighting (Willems et al., 1995).


If the new query q j is a word and its label is k, then the new factorization is where F 0,j=k is same as F 0 except that F 0,j=k (j, k) = 1.

we obtained a new factorization using the labeled words.


Similarly, if the new query q j is a document, then the new factorization is where G 0,j=k is same as G 0 except that G 0,j=k (j, k) = 1.

we obtained a new factorization using the labeled documents.


2 Compound-ing is another type of word formation that is common in Japanese (Tsujimura, 2006).

noun compounds are frequently produced by merging two or more nouns together.


This paper empirically demonstrates that splitting accuracy can be significantly improved by extracting such paraphrases from unlabeled text, the Web in our case, and then using that information for constructing splitting models.

two types of paraphrases are investigated in this paper.


The first sense selects a weapon (e.g., gun, bullet, rocket), while the second sense selects a person (e.g., employee, coach, apprentice).

we employ tiered clustering (Reisinger and Mooney, 2010) using the words in the predicate's context.


We further propose three methods for enhancing the LM LL baseline using P x (e, o) to approximate Equation 8.

a naive method for including information from our cLDa model is to consider the corpus probability, P c (v, e, o) and the coercion probability, P x (e, o), to be independent: the rank of an interpretation is dictated by the unweighted combination of its corpus probability P c and its coercion probability P x .


We therefore carry out studies to investigate this issue.

for the original four Wikipedia features, we create a bag-of-words feature that simply merges all feature types (i.e., all edges in Figure 1 will have the same label).


We show that the intuition for including these notions in WordNet-based similarity measures does not always stand up to empirical examination.

the traditional definitions of depth and density as ordinal integer values in the hierarchical structure of WordNet does not always correlate with human judgment of lexical semantic similarity, which imposes strong limitations on their contribution to an accurate similarity measure.


It is visually quite noticeable that the actual quantity denoting how deep a node resides in WordNet is conflated at depth values below 5 or above 14.

the distribution makes it rather inaccurate to say, for instance, that a node of depth 4 is twice as deep as a node of depth 2.


Secondly, even on the range of depth values with higher correlation with similarity, there is no definitive sufficient and necessary relation between depth and similarity (hence the upper triangle instead of a sloped line or band).

semantically more similar words are not necessarily deeper in the WordNet hierarchy.


This means that for 90% of the LCS's, there are only three integer values for density to distinguish the varying degrees of similarity.

such a range might be too narrow to have any real distinguishing power over similarity.


To avoid this problem, we "re-curve" depth value to the cumulative distribution.

if we take the histogram distribution of depth value in Figure 2 as a probability density function, our approach is to project cardinal depth values onto its cumulative distribution function.


2.2.1), for all subsequences of children c n 1 ( I 1j ).

pTK is a summation of an exponential number of STKs, which are valid kernels.


In this paper we consider generative models of lexical choice that assign a probability to a particular word appearing in a given linguistic context.

we follow recent work (Dinu and Lapata, 2010;Ó Séaghdha, 2010;Ritter et al., 2010) in assuming a latent variable model that associates contexts with distributions over a shared set of variables and associates each variable with a distribution over the vocabulary of word types: The set of latent variables Z is typically much smaller than the vocabulary size; this induces a (soft) clustering of the vocabulary.


Hence we follow the standard methodology of fixing parameters through cross validation.

we partition the data into five folds, four of which are used for training and one hold-out fold is used for testing.


We address the under-investigated problem of automatically determining the information status of discourse entities.

we extend Nissim's (2006) machine learning approach to information-status determination with lexical and structured features, and exploit learned knowledge of the information status of each discourse entity for coreference resolution.


Hence, the NPs from the same  Unlike Nissim (2006), we partition the 147 dialogues (rather than the instances) into a training set (117 dialogues) and a test set (30 dialogues).

we do not randomize the instances, as we believe that it represents an unrealistic evaluation setting, for the following reasons.


Whether an entity is "generally known" may be easily determined by a human but not by a machine, since world knowledge is involved in the decision process.

nissim's feature set does not contain any features that encode the notion of a "generally known" entity.


As a result, we propose to acquire this kind of world knowledge automatically from annotated data.

we augment Nissim's feature set with the set of unigrams that appear in the training data.


Note, however, that while we want to use a parse tree directly as a feature, we do not want to use the entire parse tree as a feature.

while using the entire parse tree enables a richer representation of the syntactic context than using a partial parse tree, the increased complexity of the tree also makes it more difficult for the SVM learner to make generalizations.


To strike a better balance between having a rich representation of the context and improving the learner's ability to generalize, we extract a substructure from a parse tree and use it as the value of the structured feature of an instance.

given an instance corresponding to discourse entity e, we extract the substructure from the parse tree containing e as follows.


If we want our learner to make use of more than one of these types of features, we need to employ a composite kernel to combine them.

we define and employ the following composite kernel: where F 1 and F 2 are the full set of features that represent the two entities under consideration, and K 1 and K 2 are the kernels we are combining.


First, as noted earlier, Nissim partitioned the dialogues into training and test folds in a different way than us.

original Nissim and the remaining three systems were not evaluated on the same set of test instances.


Comparing Baseline and Baseline+Lexical, we see that augmenting Nissim's feature set with lexical features improves the F-measure scores on all three classes.

the F-measure and recall for med rise considerably by 3.0 and 7.8, respectively.


Anaphoricity determination refers to the task of determining whether an NP is anaphoric or not, where an NP is considered anaphoric if it is part of a (nonsingleton) coreference chain but is not the head of the chain (Ng and Cardie, 2002).

an  NP is anaphoric if and only if it has an antecedent.


They can also be categorized based on whether they are relational or not.

relational features capture the relationship between NP j and NP k , whereas non-relational features capture the linguistic property of one of these NPs.


's (2001) method for creating training instances.

we create (1) a positive instance for each anaphoric NP NP k and its closest antecedent NP j ; and (2) a negative instance for NP k paired with each of the intervening NPs, NP j+1 , NP j+2 , .


4 After training, the classifier is used to identify an antecedent for an NP in a test text.

each NP, NP k , is compared in turn to each preceding NP, NP j , from right to left, and selects NP j as its antecedent if the pair is classified as coreferent.


(2008)) and the mention-ranking model (e.g., Denis and Baldridge (2008)).

the CR model ranks the preceding clusters for an active NP so that the highest-ranked cluster is the one to which the active NP should be linked.


The training/test split we use in the coreference experiments is the same as that in the informationstatus experiments.

we use the training set to train both the information-status classifier and our coreference models, apply the informationstatus classifier to each discourse entity in the test set, and have the coreference models resolve all and only those NPs that are labeled as old by the information-status classifier.


Given a goldstandard (i.e., key) partition, KP , and a systemgenerated (i.e., response) partition, RP , B 3 computes the recall and precision of each NP and averages these values at the end.

for each NP, NP j , B 3 first computes the number of NPs that appear in both KP j and RP j , the clusters containing NP j in KP and RP , respectively, and then divides this number by |KP j | and |RP j | to obtain the recall and precision of NP j , respectively.


Given this observation, we hypothesize that the answer to the aforementioned question depends in part on the accuracy of informationstatus classification.

the higher the accuracy of information-status classification is, the more likely the F-measure of the downstream coreference model will improve.


We examined the problem of automatically determining the information status of discourse entities in spoken dialogues.

we augmented Nissim's feature set with two types of features: lexical features, which capture in a shallow manner world knowledge implicitly encoded in the annotated data; and syntactic parse trees, which provide a richer representation of the syntactic context in which a discourse entity appears than grammatical roles.


For instance, in the case of the connotative predicate "prevent", there is strong selectional preference on negative connotation with respect to the thematic role (semantic role) "THEME".

statistically speaking, people tend to associate negative connotation with the THEME of "prevent", e.g., "prevent cancer" or "prevent war", rather than positive connotation, e.g., "prevent promotion".


That is, statistically speaking, people tend to associate negative connotation with the THEME of "prevent", e.g., "prevent cancer" or "prevent war", rather than positive connotation, e.g., "prevent promotion".

even though it is perfectly valid to use words with positive connotation in the THEME role of "prevent", statistically more dominant connotative polarity is negative.


for each connected pair of a predicate p and an argument a, there are two edges in opposite directions: e(p → a) and e(a → p).


In this case, we explore the use of asymmetric weights using conditional probability.

we define weights as follows: w(a → p) := P (p|a) = P (p, a) P (a) Having defined the graph structure, next we explore algorithms that analyze graph centrality via random walks.


In particular, we define weights as follows: w(a → p) := P (p|a) = P (p, a) P (a) Having defined the graph structure, next we explore algorithms that analyze graph centrality via random walks.

we investigate the use of HITS algorithm (Section 4), and PageRank (Section 5).


Also note that for each technique, we construct two separate graphs G + and G − corresponding to positive and negative polarity respectively.

g + learns positively connotative predicates and arguments, while g − learns negatively connotative predicates and arguments.


In this method, instead of truncating the graph, we simply emphasize the important portion of the graph via edge weights.

we assign high weights to those edges that connect a seed predicate with an argument, while assigning low weights for those edges that connect to a predicate outside the seed set.


PageRank -will be able to discern valid relations from noise, by focusing on the important part of the graph.

we expect that good predicates will be supported by good arguments, and vice versa, thereby resulting in a reliable set of predicates and arguments that are mutually supported by each other.


Next we perform extrinsic evaluation to quantify the practical value of our connotation lexicon in concrete sentiment analysis applications.

we make use of our connotation lexicon for binary sentiment classification tasks in two different ways: • Unsupervised classification by voting.


Its main characteristics are the possibility to include a huge number of features, like the Maximum Entropy (ME) model, but computing global conditional probabilities normalized at sentence level, instead of position level like in ME.

this last point results very effective since it solves the label bias problem, as pointed out in (Lafferty et al., 2001).


Z is a probability normalization factor in order to model well defined probability distribution: herec n−1 andc n are the concepts hypothesized for the previous and current words, ).

the CRF model used for the Attribute-Value Extraction phase learns in the same way the conditional probability we use attributes-words concatenations on the source side and attribute values on the target side.


the more correct hypothesis with respect to a reference annotation and a given metric.

we compute the edit distance of each hypothesis in the list, with respect to the manual annotation taken from the corpus.


In order to combine the SIM score computed by the rule-based AVE module with the score provided by the CRF AVE model, we consider per-concept scores from both approaches.

we formalize the definition of the SIM metric above on a concept c i as SIM (c i , w 1,...,m i ).


Each type correspond to features at any position with respect to the target   In figure 2(a), 2(b) and 2(c) we show the curves resulting from optimization of parameters of reranking models.

we optimized the M kernel parameter (µ decay factor, see (Moschitti, 2006) for details), and the C SVM parameter, i.e.


We can reach performances of state-of-the-art models, improving them in some cases, especially on automatic transcriptions coming from ASR (speech input).

the effectiveness of hypotheses selection is shown reporting the improvement of the Oracle Error Rate on the 10-best hypotheses list.


It provides a flexible framework to incorporate different knowledge sources in the form of feature constraints.

mELm takes the form of (2), for word w following history h, we have the following probability definition, f i is the i th feature function defined over the word-history pair, θ i is the feature weight associated with f i .


The variables S jl is a binary indicator of the sentence.

s jl = 1 if the sentence is included in the final summary, and s jl = 0 otherwise.


The alignment approach, on the other hand, can be summarised thus: Speakers align the forms of reference they use to be similar or identical to references that have been used before.

once a form of reference to the intended referent has been established, they tend to re-use that form of reference, or perhaps an abbreviated version of it.


To assess the output quality of the resulting sentence compression system, we compare it to two state-ofthe-art sentence compression systems.

we compare against our implementation of Clarke and Lapata (2008)'s compression model which uses a series of constraints in an integer linear programming (ILP) solver, and Cohn and Lapata (2007)'s tree transducer toolkit (T3) which learns a synchronous tree substitution grammar (STSG) from paired monolingual sentences.


From Table 1 and 2, we can have an interesting finding: it seems that the harder we filter the POS tag space, the higher tagging accuracy we get.

giving the joint model less flexibility of choosing POS tags leads to better tagging performance.


8 One common characteristic of these ambiguous pairs is that the local or even whole syntactic structure will be destructed if the wrong tag is chosen.

resolving these ambiguities is critical and helpful from the parsing viewpoint.


From the table, we can see the joint model can achieve a large error reduction (0.8∼4.0%) for all the patterns "X → X".

the joint model can do better given the correct tags than the pipelined method.


Given a PPT τ , let A(τ ) be the set of all possible ASTs.

our goal is to find the AST which has the highest probability given the PPT: The conditional probability of an AST is defined as: P (α|τ ) is modeled as the product of the probabilities of the ranking of each preterminal, which is in turn expressed as the product of the probabilities of the pairwise ordering decisions.


The second row considers observations where o ′ is embedded syntactically within o, and predicts whether R(o) ≫ o ′ .

the problem is to determine whether a syntactically embedded operator remains scopally embedded, or whether it has inverse scope (see Section 3.3).


As a result, the total number of possible orderings is typically less than 7!, and a beam size of 10 4 is sufficient to compute an exact ordering.

due to the size restrictions, in most cases, beam search is equivalent to exact (exhaustive) search.


Note that τ , α and α * share the same set of preterminals, but may associate different operators with them.

we say that p is correct in α * , if it is associated with the same set of operators as in α, and the preterminals are identical, both in terms of the set of operators and the ordering between pairs of operators.


The probability We also assume that p(d) = 1 when d is an empty sequence of rules.

the probability p(w) of a string w is the sum of all complete derivations deriving that string from the start symbol: With this notation, consistency of a PCFG is de-1214 fined as the condition: a PCFG is consistent if the sum of probabilities of all complete derivations starting with S is 1.


Such systems can be solved one by one, in a bottom-up order.

if one strongly connected component contains nonterminal A, and another contains nonterminal B, where A * ⇒ uBα for some u, α, then the system for the latter component must be solved first.


Our investigations so far have not found a positive answer to this second question.

the systems of equations for C 1 and C 3 change fundamentally if the infix is extended by one more symbol, which seems to at least make incremental computation very difficult, if not impossible.


However, in standard practice for natural language parsing, transitions are always specified by some finite mean.

the definition of each transition depends on some finite window at the top of the stack and some finite window at the beginning of the buffer in each configuration.


This means that longer computations over substrings of w are built by combining shorter ones.

the inference rule sh a j asserts the existence of I-computations consisting of a single sh a j transition.


For our work, we build on a baseline system that combines n-gram and lexical features (Bergsma et al., 2010).

this paper makes the following contributions: 1.


We partitioned our dataset by user into three distinct subsets, training, development, and test, with sizes as indicated in Figure 1.

all the tweets from each user are in a single one of the three subsets.


We propose a general framework for identifying expert investors, and use it as a basis for several models that predict stock rise from stock microblogging messages (stock tweets).

we present two methods that combine expert identification and per-user unsupervised learning.


This paper investigated the novel task of finding expert investors in online stock forums.


Intuitively, each neighboring vertex votes for the cluster it is currently assigned to, where the strength of the vote is determined by the similarity (i.e., edge weight) to the vertex being updated.

the label l i of vertex v i is thus updated according to the following equation: where for each label we compute a score by summing together the weights of edges to neighboring vertices with that label and select the label with the maximal score.


Determining the similarity function φ without access to labeled training data poses a major difficulty which we resolve by relying on prior linguistic knowledge.

we measure the similarity of argument instances based on three simple and intuitive criteria: (1) whether the instances are lexically similar; (2) whether the instances occur in the same syntactic position; and (3) whether the instances occur in the same frame (i.e., are arguments in the same clause).


And although a human judge could determine the optimal termination point based on several criteria such as clustering quality or the number of clusters, we used a development set instead for the sake of reproducibility and comparability.

we optimized θ min on the CoNLL test set and obtained best results with θ min = 1 3 .


Compared with previous works, the advantages of using G as sentiment representation are: 1) for individual opinions, the modifiers will collect more information than using opinion expression alone.

3 We don't define any "label" on vertices: if two span of text satisfy a relation in L, they are chosen to be vertices and an edge with proper label will appear in E. vertices are identified by checking whether there exist relations among them.


The average in-degree of a vertex is 1.03 in our corpus, thus the graph is almost a rooted tree.

the cases that a modifier connects to more than one opinion expression rarely occur comparing with those vertices which have a single parent.


We cast tuning as a ranking problem (Chen et al., 2009), where the explicit goal is to learn to correctly rank candidate translations.

we follow the pairwise approach to ranking (Herbrich et al., 1999;Freund et al., 2003;Burges et al., 2005;Cao et al., 2007), in which the ranking problem is reduced to the binary classification task of deciding between candidate translation pairs.


For its candidate generation phase, MERT generates the k-best candidate translations for each source sentence according to h w , where w is the weight vector from the previous optimization phase (or an arbitrary weight vector for the first iteration).

for its optimization phase, MERT defines the loss function as follows: it prefers weight vectors w such that the gold function G scores H w 's best policy as highly as possible (if H w 's best policy is the same as G's best policy, then there is zero loss).


For each of our systems we identify two feature sets: baseline, which correspond to the typical small feature set reported in current MT literature, and extended, a superset of baseline, which adds hundreds or thousands of features.

we use 15 baseline features for PBMT, similar to the baseline features described by Watanabe et al.


For instance, comparing the outputs of systems 1 and 2 with β = 10 in Table 3 we find 45 different 1-best hypotheses, even though the BLEU score is identical.

we have 45 cases in which system 2 is not able to recover the baseline output because the 1st-pass likelihood beam β is not wide enough.


Contextdependent variation in word usage can be accounted for by leveraging multiple latent categorization systems.

cross-cutting models can be used to capture both syntagmatic and paradigmatic notions of word relatedness, breaking up word features into multiple categorization systems and then computing similarity separately for each system.


One interesting question is to what degree MVM views partition syntax and document features versus LDA topics.

to what degree do the MVM views capture purely syntagmatic or purely paradigmatic variation?


In this paper, we show that one can reduce the task of learning a semantic parser to a Probabilistic Context Free Grammar (PCFG) learning task, and more generally, that grounded learning tasks are not in principle beyond the scope of grammatical inference techniques.

we show how to formulate the task of learning a semantic parser as discussed by Chen, Kim andMooney (2008, 2010) as the task of learning a PCFG from strings.


Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors.

we detect a new semantic relation by projecting the new relation's training instances onto a lower dimension topic space constructed from existing relation detectors through a three step process.


Define dyadic spatial scales t j as At each spatial scale, the spectral band is defined as: where Λ(T ) represents any eigenvalue of T , and ε ∈ (0, 1) is a pre-defined threshold in Figure 2.

we can now associate with each of the spectral bands a vector subspace spanned by the corresponding eigenvectors: In the limit, we obtain the highest level of the resulting subspace hierarchy is spanned by the eigenvector associated with the largest eigenvalue of T .


The task of training such a classifier involves estimating the parameters in θ, given a set of labeled To do this, we use a Dirichlet prior and take the expectation of each parameter with respect to the posterior, which is a simple way to estimate a multinomial (Heckerman, 1995).

we count the fraction of times the word f k occurs in the labeled set among documents of class y j , and the prior adds m jk "hallucinated" occurrences for a smoothed version of the maximum likelihood estimate: Here, m jk is the prior for word f k under class y j , P (y j |x (i) ) ∈ {0, 1} indicates the true labeling of the ith document in the training set, and Z(f k ) is a normalization constant summing over all words in the vocabulary.


The case where there are both gold-standard trees and a task-specific objective for the entire training set is a specific instance of the larger problem that we address here.

the algorithm takes the form of an online learner where a training instance is selected and the param-eters are optimized based on the objective function associated with the instance (either intrinsic or extrinsic), thus jointly optimizing multiple objectives.


As alluded to in Section 2.2, we use a reorderingbased loss function to improve word order in a machine translation system.

we use a system of source-side reordering rules which, given a parse of the source sentence, will reorder the sentence into a target-side order (Collins et al., 2005).


To test whether such weak information can significantly improve the parsing of questions, we trained an augmented-loss parser using the training set of the QTB stripped of all dependencies except the dependency from the root to the main verb of the sentence.

for each sentence, the parser may only observe a single dependency at training from the QTB -the dependency to the main verb.


we modify the lexical learning, to produce lexemes and templates, as well as the feature space of the model, but reuse the existing parameter estimation techniques and overall learning cycle, as described in Section 7.


To help address the issue of OOV words and lexical variations, we perform clustering to group together words which are distributionally similar (Brown et al., 1992;Turian et al., 2010).

we perform hierarchical clustering using Jcluster (Goodman, 2001) on 52 million tweets; each word is uniquely represented by a bit string based on the path from the root of the resulting hierarchy to the word's leaf.


The judges labeled uninformative extractions conservatively.

if critical information was dropped from the relation phrase but included in the second argument, it is labeled correct.


REVERB has much higher precision than the other systems at nearly all levels of recall.

more than 30% of REVERB's extractions are at precision 0.8 or higher, compared to virtually none for the other systems.


Taking the trees produced by statistical parsers, we use tree cross-sections as features in a machine learning approach to determine which ones characterise non-native speaker errors.

we look at two types of parse tree substructure to use as features: horizontal slices of the trees-that is, characterising parse trees as sets of context-free grammar production rules-and the features schemas used in discriminative parse reranking.


However, unlike text to text translation, which has been extensively studied in the machine translation community, translating from logical forms into text presents additional challenges.

logical forms such as λ-expressions may have complex internal structures and variable dependencies across subexpressions.


Langkilde (2000) presented a probabilistic model for generation from a packed forest meaning representation, without concerning lexical acquisition.

we are not aware of any prior work that handles both automatic unsupervised lexical acquisition and surface realization for generation from logical forms in a single framework.


For better readability, we introduce the symbol ¡ as an alternative notation for functional application.

h ¡ f refers to (h f ) or h(f ), and h ¡ f ¡ g refers to ((h f ) g).


Following popular approaches to learning feature weights in the machine translation community (Och and Ney, 2004;Chiang, 2005), we use the minimum error rate training (MERT) (Och, 2003) algorithm to learn the feature weights that directly optimize certain automatic evaluation metric.

the Z-MERT (Zaidan, 2009) implementation of the algorithm is used in this work.


Next, we extract grammar rules on top of these λ-hybrid trees.

we extract the following three types of synchronous grammar rules, with examples given in Figure 3: 1. λ-hybrid sequence rules: They are the conventional rules constructed from one λ-production and its corresponding λ-hybrid sequence.


Experiments show that these two types of rules are important.

type 3 rules, which are able to capture longer structural dependencies, are of particular importance for generating Chinese.


Finally, we also assess the effectiveness of our model on an alternative meaning representation formalism in the form of variable-free tree structures.

we tested on the ROBOCUP dataset (Kuhlmann et al., 2004), which consists of 300 English instructions for coaching robots for soc-cer games, and a variable-free version of the GEO-QUERY dataset.


We used a rule-based method to extract temporal expressions and used Stanford parser (De Marneffe et al., 2006) to analyze association between the temporal expressions and the extractions.

we define that an extraction is associated with a temporal expression if there is an arc from the predicate of the extraction to the temporal expression in the dependency tree.